Time Heuristic Audio Control

ABSTRACT

A time heuristic audio control system, comprises a receiver for receiving time-based data from a personal computing device and a memory storing one or more sets processing parameters comprising instructions for processing the ambient sound based upon the time-based data. The system further includes a processor coupled to the memory and the receiver configured to adjust the ambient sound as directed by a selected set of processing parameters retrieved from the memory to create adjusted audio, the selected set of processing parameters retrieved based upon the time-based data and at least one speaker for outputting the adjusted audio.

NOTICE OF COPYRIGHTS AND TRADE DRESS

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. This patent document may showand/or describe matter which is or may become trade dress of the owner.The copyright and trade dress owner has no objection to the facsimilereproduction by anyone of the patent disclosure as it appears in thePatent and Trademark Office patent files or records, but otherwisereserves all copyright and trade dress rights whatsoever.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent application is a continuation of U.S. patent applicationSer. No. 15/917,472 filed Mar. 9, 2018, which is a continuation of U.S.patent application Ser. No. 15/382,475 filed Dec. 16, 2016, now U.S.Pat. No. 9,918,159 issued Mar. 13, 2018, which is a continuation of U.S.patent application Ser. No. 14/928,996 filed Oct. 30, 2015, now U.S.Pat. No. 9,560,437 issued Jan. 31, 2017, which is a continuation-in-partof U.S. patent application Ser. No. 14/681,843 filed Apr. 8, 2015, nowU.S. Pat. No. 9,524,731 issued Dec. 20, 2016, which claims the benefitof priority to U.S. Provisional Patent Application No. 61/976,794 filedApr. 8, 2014, all of which are incorporated herein by reference in theirentirety.

BACKGROUND Field

This disclosure relates generally to a system for time heuristic audiocontrol. In particular, this disclosure relates to the adjustment ofambient and secondary audio sources using time-based data.

Description of the Related Art

Audio equalization systems have existed for some time. Through thesesystems, users of personal audio devices such as Sony® Walkman® or theApple® iPod® have been able to adjust the relative volume of frequenciesin pre-recorded audio as desired. Similarly, these devices have oftenemployed pre-set memories that enable users to store preferredequalization settings or manufacturer-set pre-set settings that may havenames, such as “bass boost” or “symphony” or “super-treble” dependentupon their particular parameters. Whatever the case, users have beenrequired to either set the settings and/or store them for later use, orto select from a group of previously-stored settings as desired.

In a related field, active and passive noise cancellation to removeundesirable traits of ambient audio and personal pre-recorded audio haveexisted for some time. For example, Bose® noise cancelling headphonesare known for removing virtually all ambient sound within desiredfrequency range from an environment (e.g. airplane noise while anindividual is flying in an airplane). Simultaneously, these types ofsystems may include the capability to output audio, such as pre-recordedaudio, through one or more speakers. However, these systems typicallyare all-or-nothing systems in which all external sound is effectivelycancelled or attenuated and any pre-recorded audio is output as-is.Thus, the noise-cancelling properties are typically “enabled” or “notenabled.”

DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an environment.

FIG. 2 is block diagram of an audio processing system.

FIG. 3 is a block diagram of a personal computing device.

FIG. 4 is a functional block diagram of a portion of a time heuristicaudio system.

FIG. 5 is a functional block diagram of a time heuristic audio system.

FIG. 6 is a flow chart of a method for creating a time heuristic audiocontrol.

FIG. 7 is a flow chart of a method for altering audio processingparameters in response to a time heuristic audio control.

Throughout this description, elements appearing in figures are assignedthree-digit reference designators, where the most significant digit isthe figure number where the element is introduced and the two leastsignificant digits are specific to the element. An element not describedin conjunction with a figure has the same characteristics and functionas a previously-described element having the same reference designator.

DETAILED DESCRIPTION

An individual may wish to pre-define, either through overt action, orpreferably, through machine-learning principles, a set of audioprocessing parameters that are to be used when the individual is knownto be at a particular event, location, or environment. For example, whenattending a series of concerts at a particular venue, a user mayrepeatedly select one predetermined set of audio processing parameters.These parameters may, for example, adjust equalization settings,introduce a reverb and perform active noise cancellation on aspects ofsound while not eliminating others (e.g. eliminating human voices whilenot eliminating any other ambient sound). After a user has selectedthose same settings several times when in the particular venue, thosesettings may be stored as an automatically-selected set of processingparameters when calendar data in the individual's personal computingdevice (e.g. an iPhone®) indicates that the user is at that same concertvenue. On the next visit to that location, based upon time-based datalike calendar data, the same set of processing parameters may beselected automatically.

As used herein, the phrases “ambient sound” or “ambient audio” meansound in the physical location where a user of the time heuristic audiocontrol system is present. Ambient sound is further audio that may beheard, either by the user's ears in that physical location or while inthat physical location with the aid of audio-enhancing technologies.Ambient sound is distinguished from “secondary audio” or “secondarysound” in that secondary sound and secondary audio as used herein meansaudio that is not audible in the physical location where the user of thecombined ambient and secondary audio system is present either by humansor by the aid of audio-enhancing technologies. Secondary audio can comefrom many different types of sources, but it is distinctly not in thepresent physical environment audible to a user of the system. Bothambient sound and secondary audio may be limited to applications forin-ear earbuds or over-the-ear headphones that would, without thereproduction of ambient sound by speakers within the system, otherwisesignificantly reduce or virtually eliminate ambient sound.

As used herein “time-based data” means data that is dependent upon orderived from the present, a past, or a future time. Calendar entriessuch as appointments, meetings, and previously-scheduled events areexamples of time-based data. Similarly, “time-based data” may beobtained from other sources such as text messages, simple messageservice messages, instant messaging services, group messaging services,email, and other text-based communications such that, for example, dataindicating a plan to meet at a particular location at a pre-determinedtime may comprise “time-based data.”

Similarly, data attached to any one of these formats may also betime-based data. For example, an email may include as an attachmente-tickets or tickets in PDF (portable document format) form to an eventat a concert venue with date and time information appearing on the faceof the tickets. This date and time information is time-based data.Time-based data may also simply be the present time as determined by aclock. In situations in which a user habitually performs an action at aknown time every weekday or every third Tuesday or othersimilarly-predictable interval, the present time, noted over the courseof multiple occurrences may act as time-based data. Location data, suchas GPS (global positioning system) data, or assisted GPS data isspecifically excluded from the meaning of “time-based data” as usedherein.

Description of Apparatus

Referring now to FIG. 1, an environment 100 may include a cloud 130 anda time heuristic audio system 140. In this context, the term “cloud”means a network and all devices that may be accessed by the timeheuristic audio system 140 via the network. The cloud 130 may be a localarea network, wide area network, a virtual network, or some other formof network together with all devices connected to the network. The cloud130 may be or include the Internet. The devices within the cloud 130 mayinclude one or more servers 132 and one or more personal computingdevices 134.

The time heuristic audio system 140 includes an audio processing system110 and a personal computing device 120. While the personal computingdevice 120 is shown in FIG. 1 as a smart phone, the personal computingdevice 120 may be a smart phone, a desktop computer, a mobile computer,a wrist-computer, smartwatch, smartwatch-like device, a tablet computer,or any other computing device that is capable of performing theprocesses described herein. In some cases, some or all of the personalcomputing device 120 may incorporated within the audio processing system110 or some or all of the audio processing system 110 may beincorporated into the personal computing device.

The personal computing device 120 may include one or more processors andmemory configured to execute stored software instructions to perform theprocesses described herein. For example, the personal computing device120 may run an application program or “app” to perform some or all ofthe functions described herein. The personal computing device 120 mayinclude a user interface comprising a display and at least one inputdevice such as a touch screen, microphone, keyboard, and/or mouse. Thepersonal computing device 120 may be configured to perform geo-location,which is to say to determine its own location and to thereby generatelocation data. Geo-location may be performed, for example, using aGlobal Positioning System (GPS) receiver or by some other method.

The audio processing system 110 may communicate with the personalcomputing device 120 via a first wireless communications link 112. Thefirst wireless communications link 112 may use a limited-range wirelesscommunications protocol such as Bluetooth®, Wi-Fi®, ZigBee®, or someother wireless Personal Area Network (PAN) protocol. The personalcomputing device 120 may communicate with the cloud 130 via a secondcommunications link 122. The second communications link 122 may be awired connection or may be a wireless communications link using, forexample, the WiFi® wireless communications protocol, a mobile telephonedata protocol, or another wireless communications protocol.

Optionally, the audio processing system 110 may communicate directlywith the cloud 130 via a third wireless communications link 114. Thethird wireless communications link 114 may be an alternative to, or inaddition to, the first wireless communications link 112. The thirdwireless connection 114 may use, for example, the WiFi® wirelesscommunications protocol, Bluetooth® or another wireless communicationsprotocol. Still further, the audio processing system 110 may communicatewith the cloud 130 through the second communications link 122 of thepersonal computing device 120 and the first communications link 112.

FIG. 2 is block diagram of an audio processing system 200. This may bethe audio processing system 110 of FIG. 1. The audio processing system200 may include a microphone 210, a preamplifier 215, ananalog-to-digital (A/D) converter 220, a wireless interface 225, aprocessor 230, a memory 235, an analog signal by digital-to-analog (D/A)converter 240, and amplifier 245, a speaker 250, and a battery (notshown), all of which may be contained within a housing 290. Some or allof the microphone 210, the preamplifier 215, the analog-to-digital (A/D)converter 220, the wireless interface 225, the processor 230, the memory235, the analog signal by digital-to-analog (D/A) converter 240, and theamplifier 245, a speaker 250 elements may be integrated into one or moreintegrated microchips or systems-on-chips.

The housing 290 may be configured to interface with a user's ear byfitting in, on, or over the user's ear such that ambient sound is mostlyexcluded from reaching the user's ear canal and processed soundgenerated by the audio processing system 200 is coupled into the user'sear canal. The housing 290 may have a first aperture 292 for acceptingambient sound and a second aperture 294 to allow processed sound to beoutput into the user's outer ear canal.

The housing 290 may be, for example, an earbud housing. The term“earbud” means an apparatus configured to fit, at least partially,within and be supported by a user's ear. An earbud housing typically hasa portion that fits within or against the user's outer ear canal. Anearbud housing may have other portions that fit within the concha orpinna of the user's ear.

The microphone 210 converts received sound 205 (e.g. ambient sound) intoan electrical signal that is amplified by preamplifier 215 and convertedinto digital sound 222 by A/D converter 220. The digital sound 222 maybe processed by processor 230 to provide digitized processed sound 232.The processing performed by the processor 230 will be discussed in moredetail subsequently. The digitized processed sound 232 is converted intoan analog signal by D/A converter 240. The analog signal output from D/Aconverter 240 is amplified by amplifier 245 and converted into processedoutput sound 255 by speaker 250.

The depiction in FIG. 2 of the audio processing system 200 as a set offunctional blocks or elements does not imply any corresponding physicalseparation or demarcation. All or portions of one or more functionalelements may be located within a common circuit device or module. Any ofthe functional elements may be divided between two or more circuitdevices or modules. For example, all or portions of theanalog-to-digital (A/D) converter 220, the wireless interface 225, theprocessor 230, the memory 235, the analog signal by digital-to-analog(D/A) converter 240, and the amplifier 245 may be contained within acommon signal processor circuit device.

The microphone 210 may be one or more transducers for converting soundinto an electrical signal that is sufficiently compact for use withinthe housing 290.

The preamplifier 215 may be configured to amplify the electrical signaloutput from the microphone 210 to a level compatible with the input ofthe A/D converter 220. The preamplifier 215 may be integrated into theA/D converter 220, which, in turn, may be integrated with the processor230. In the situation where the audio processing system 200 containsmore than one microphone, a separate preamplifier may be provided foreach microphone.

The A/D converter 220 may digitize the output from preamplifier 215,which is to say convert the output from preamplifier 215 into a seriesof digital ambient sound samples at a rate at least twice the highestfrequency present in the ambient sound. For example, the A/D convertermay output digital sound 222 in the form of sequential sound samples atrate of 40 kHz or higher. The resolution of the digitized sound 222(i.e. the number of bits in each sound sample) may be sufficient tominimize or avoid audible sampling noise in the processed output sound255. For example, the A/D converter 220 may output digitized sound 222having 12 bits, 14, bits, or even higher resolution. In the situationwhere the audio processing system 200 contains more than one microphonewith respective preamplifiers, the outputs from the preamplifiers may bedigitized separately, or the outputs of some or all of the preamplifiersmay be combined prior to digitization.

The wireless interface 225 may provide the audio processing system 200with a connection to one or more wireless networks 295 using alimited-range wireless communications protocol such as Bluetooth®,Wi-Fi®, ZigBee®, or other wireless personal area network protocol. Thewireless interface 225 may be used to receive data such as parametersfor use by the processor 230 in processing the digital ambient sound 222to produce the digitized processed sound 232. The wireless interface 225may be used to receive digital sound, such as audio from a secondaryaudio source. Alternatively, a hardware interface such as an audio inputjack of various known types (not shown) may enable input of digitalsecondary audio to the processor 230. The wireless interface 225 mayalso be used to export the digitized processed sound 232, which is tosay transmit the digitized processed sound 232 to a device external tothe ambient audio processing system 200. The external device may then,for example, store and/or publish the digitized processed sound, forexample via social media.

The processor 230 may include one or more processor devices such as amicrocontroller, a microprocessor, and/or a digital signal processor.The processor 230 can include and/or be coupled to the memory 235. Thememory 235 may store software programs, which may include an operatingsystem, for execution by the processor 230. The memory 235 may alsostore data for use by the processor 230. The data stored in the memory235 may include, for example, digital sound samples and intermediateresults of processes performed on the digital ambient sound 222. Thedata stored in the memory 235 may also include a user's listeningpreferences, and/or rules and parameters for applying particularprocesses to convert the digital sound 222 into the digitized processedsound 232 prior to output. The memory 235 may include a combination ofread-only memory, flash memory, and static or dynamic random accessmemory.

The D/A converter 240 may convert the digitized processed sound 232 fromthe processor 230 into an analog signal. The processor 230 may outputthe digitized processed sound 232 as a series of samples typically, butnot necessarily, at the same rate as the digital sound 222 is generatedby the A/D converter 220. The analog signal output from the D/Aconverter 240 may be amplified by the amplifier 245 and converted intoprocessed output sound 255 by the speaker 250. The amplifier 245 may beintegrated into the D/A converter 240, which, in turn, may be integratedwith the processor 230. The speaker 250 can be any transducer forconverting an electrical signal into sound that is suitably sized foruse within the housing 290.

The battery (not shown) may provide power to various elements of theaudio processing system 200. The battery may be, for example, a zinc-airbattery, a lithium ion battery, a lithium polymer battery, a nickelcadmium battery, or a battery using some other technology.

FIG. 3 is a block diagram of an exemplary personal computing device 300which may be suitable for the personal computing device 120 within thetime heuristic audio system 140. As shown in FIG. 3, the personalcomputing device 300 includes a processor 310, memory 320, a userinterface 330, and a communications interface 340. Some of theseelements may or may not be present, depending on the implementation.Further, although these elements are shown independently of one another,each may, in some cases, be integrated into another.

The processor 310 may be or include one or more microprocessors,microcontrollers, digital signal processors, application specificintegrated circuits (ASICs), or a system-on-a-chip (SOCs). The memory320 may include a combination of volatile and/or non-volatile memoryincluding read-only memory (ROM), static, dynamic, and/ormagnetoresistive random access memory (SRAM, DRM, MRAM, respectively),and nonvolatile writable memory such as flash memory.

The communications interface 340 includes at least one interface forwireless communications with external devices. The communicationsinterface 340 may include one or more of a cellular telephone networkinterface 342, a wireless Local Area Network (LAN) interface 344, and/ora wireless PAN interface 346. The cellular telephone network interface342 may use one or more of the known 2G, 3G, and 4G cellular dataprotocols. The wireless LAN interface 344 may use the WiFi® wirelesscommunications protocol or another wireless local area network protocol.The wireless PAN interface 346 may use a limited-range wirelesscommunications protocol such as Bluetooth®, Wi-Fi®, ZigBee®, or someother wireless personal area network protocol. When the personalcomputing device is deployed as part of a time heuristic audio system,such as the time heuristic audio system 140, the wireless PAN interface346 may be used to communicate with one or more audio processing systems110. The cellular telephone network interface 342 and/or the wirelessLAN interface 344 may be used to communicate with the cloud 130.

The communications interface 340 may include radio-frequency circuits,analog circuits, digital circuits, one or more antennas, and otherhardware, firmware, and software necessary for communicating withexternal devices, such as an audio processing system 110. Thecommunications interface 340 may include one or more processors toperform functions such as coding/decoding, compression/decompression,and encryption/decryption as necessary for communicating with externaldevices using selected communications protocols. The communicationsinterface 340 may rely on the processor 310 to perform some or all ofthese function in whole or in part.

The memory 320 may store software programs and routines for execution bythe processor. These stored software programs may include an operatingsystem such as the Apple® iOS or Android® operating systems. Theoperating system may include functions to support the communicationsinterface 340, such as protocol stacks, coding/decoding,compression/decompression, and encryption/decryption. The storedsoftware programs may include an application or “app” to cause thepersonal computing device to perform portions of the processes andfunctions described herein.

The user interface 330 may include a display and one or more inputdevices including a touch screen.

FIG. 4 shows a functional block diagram of a portion of a time heuristicaudio system 400, which may be the system 140. Some or all of elementsof the functional block diagram may be encompassed within the audioprocessing system 110 or within the personal computing device 120. Thatis to say, the functions and processing described with reference to FIG.4 may take place in whole or in part in one or both of these devices,with the final sound being delivered to one or more speakers within theaudio processing system 110.

In the system 400, digitized ambient sound may be received, for example,from an A/D converter such as the A/D converter 220. The digitizedambient sound is processed by an audio processing function 410implemented by a processor such as the processor 230. The processorperforming the audio processing function may include one or moreprocessor devices such as a microcontroller, a microprocessor, and/or adigital signal processor. The audio processing function 410 may includefiltering, equalization, compression, limiting, and other processes.Filtering may include high-pass, low-pass, band-pass, and band-rejectfiltering. Equalization may include dividing the ambient sound into aplurality of frequency bands and subjecting each of the bands to arespective attenuation or gain. Equalization may be combined withfiltering, such as a narrow band-reject filter to suppress a particularobjectionable component of the ambient sound. Compression may be used toalter the dynamic range of the ambient sound such that louder sounds areattenuated more than softer sounds. Compression may be combined withfiltering or with equalization such that louder frequency bands areattenuated more than softer frequency bands. Limiting may be used toattenuate louder sounds to a predetermined loudness level withoutattenuating softer sounds. Limiting may be combined with filtering orwith equalization such that louder frequency bands are attenuated to adefined level while softer frequency bands are not attenuated orattenuated by a smaller amount. Techniques for implementing filters,compressors, and limiters are known to those of skill in the art ofdigital signal processing.

The audio processing function 410 may also include adding echo orreverberation to the ambient sound. The audio processing function 410may also include detecting and cancelling an echo in the ambient sound.The audio processing function 410 may further include noise reductionprocessing. Techniques to add or suppress echo, to add reverberation,and to reduce noise are known to those of skill in the art of digitalsignal processing. The audio processing function 410 may include musiceffects such as chorus, pitch shifting, flanging, and/or “vinyl”emulation (adding scratches and pops to emulation vinyl records).Techniques to add these music effects are known to those of skill in theart of digital signal processing.

The audio processing function 410 may be performed in accordance withprocessing parameters 425 provided from audio parameter memory 460 andlocation based parameter memory 430. Multiple processing parameters 425may be created and stored in the audio parameter memory 460.

The processing parameters 425 may define the type and degree of one ormore processes to be performed on the digitized ambient sound or uponany secondary audio feed. For example, the processing parameters 425 maydefine filtering by a low pass filter with a particular cut-offfrequency (the frequency at which the filter start to attenuate) andslope (the rate of change of attenuation with frequency) and/orcompression using a particular function (e.g. logarithmic). For furtherexample, the processing parameters 425 may define the way in which asecondary audio feed is overlaid or combined with the digitized ambientsound. The number and format of the processing parameters 425 may varydepending on the type of audio processing to be performed.

The audio processing function 410 may be defined, in part, based onanalysis of the ambient sound by an analysis function 420, which may beimplemented by the same processor, or a different processor, as theaudio processing function 410. The analysis function 420 may analyze thedigitized ambient sound to determine, for example, an overall (i.e.across the entire audible frequency spectrum) loudness level or theloudness level within particular frequency bands. For further example,the analysis function 420 may transform the digitized ambient soundand/or the digitized sound output from the audio processing function 410into the frequency domain using, for example, a windowed Fouriertransform. The transformed sound may then be analyzed to determine thedistribution of the ambient sound within the audible frequency spectrumand/or to detect the presence of dominant sounds at particularfrequencies. The analysis function 420 may perform other analysis todetermine other characteristics of the digitized ambient sound.

A portion of the processing parameters 425 for the audio processingfunction 410 may define processes dependent on the analysis of theambient sound. For example, a first processing parameter may requirethat the overall loudness of the processed sound output from the timeheuristic audio system 400 be less than a predetermined value. Theanalysis function 420 may determine the overall loudness of the ambientsound and the audio processing function 410 may than provide anappropriate amount of overall attenuation

The processing parameters 425 may be received or retrieved from severalsources. The processing parameters 425 may be received from a user ofthe time heuristic audio system 400. The user may manually enterprocessing parameters via a user interface 470, which may be the userinterface of a personal computing device such as the personal computingdevice 120. Alternatively, a microphone accessible to the audioprocessing function 410 (such as mic 210) or a microphone (not shown) inportable computing device 300 may provide input that is used inconjunction with the audio processing function 410 and other processingparameters 425 to adjust the time heuristic audio system 400.

The processing parameters 425 may be received from a device or devicesavailable via a computer network or otherwise available within the cloud130. For example, a website accessible via the cloud 130 may listrecommended sets of processing parameters for different venues, bands,sporting events, and the like. These processing parameters 425 may begenerated, in part, based upon feedback regarding the ambient sound frommultiple time heuristic audio systems like time heuristic audio system140 in communication with one another using the cloud 130. Similarly, asetting change on one of a group of interconnected ambient and secondaryaudio systems may be propagated to all.

The processing parameters 425 may be, at least in part, location-based,which is to say the processing parameters 425 may be associated with acurrent location of the time heuristic audio system 400 as determinedbased upon location data 435 received, for example, from a GPS. Thecurrent location may be a specific location (e.g. “user's living room”,“user's office”, “Fenway Park”, “Chicago O'Hare Airport”, etc.) or ageneric location (e.g. “sporting event”, “dance club”, “concert”,“airplane”, etc.). A location-based parameter memory 430 may store oneor more sets of location-based processing parameters in association withdata defining respective locations. The appropriate processingparameters may be retrieved from location-based parameter memory 430based on location data 435 identifying the current location of the timeheuristic audio system 400.

The location data 435 may be provided by a geo-location function 440.The geo-location function may use GPS, cell tower signal strength, aseries of relative-location calculations based upon interconnected timeheuristic audio systems 140 or some other technique for identifying thecurrent location. The location data 435 may be provided by the user viathe user interface 470. For example, the user may select a location froma list of locations for which processing parameters are stored in thelocation-based parameter memory 430. The location data 435, particularlyfor a generic location, may be retrieved from a cloud external to thetime heuristic audio system 400. The location data 435 may obtained bysome other technique.

The one or more sets of location-based processing parameters may havebeen stored in the location-based parameter memory 430 during priorvisits to the corresponding locations. For example, the user of the timeheuristic audio system 400 may manually set processing parameters fortheir home and save the processing parameters in the location-basedparameter memory 430 in association with the location “home”. Similarly,the user may set and save processing parameters for other locations(e.g. “work”, “patrol”, etc.). Upon returning to these locations (or tolocations defined in the negative (not “home”, not “work”, etc.), thecorresponding processing parameters may be automatically retrieved fromthe location-based parameter memory 430.

The processing parameters 425 may be based, at least in part, uponambient sound, which is to say the processing parameters 425 may beassociated with particular characteristics of the ambient sound. Thetime heuristic audio system 400 may “listen” to the ambient sound andlearn what filter parameters the user sets in the presence of variousambient sound characteristics. Once the ambient sound has beencharacterized, the time heuristic audio system 400 may select or suggestprocessing parameters 425 appropriate for the characteristics of thecurrent ambient sound.

For example, an audio parameter memory 460 may store one or more audiosound profiles identifying respective sets of processing parameters 425to be applied to ambient audio as those processing parameters 425 havebeen previously defined by the user, by a manufacturer, by a supervisor,or by an organization of which a wearer is a member for use in aparticular environment or situation. Each stored audio sound profile mayinclude characteristics such as, for example, frequencies to attenuateor increase in volume, instructions to emphasize sounds that alreadystand out from the overall ambient sound environment (e.g. gunshots,footsteps, dogs barking, human voices, whispers, etc.) whiledeemphasizing (e.g. decreasing the overall volume) other ambient sounds,elements of sound to emphasize, aspects to superimpose over ambientaudio or identifications of databases and algorithms from which to drawaudio for superimposition over ambient audio, locational feedbackalgorithms for emphasizing locations of certain sounds or frequencyranges, sources of live audio to superimpose over ambient sound orother, similar profiles.

An ambient sound characterization function 450, which may work inconjunction with or in parallel to the analysis function 420, maydevelop an ambient sound profile of the current ambient sound. Theprofile determined by the ambient sound characterization function 450may be used to retrieve an appropriate sound profile, including theassociated processing parameters 425 from the audio parameter memory460. This retrieval may rely in part upon the location data 435 andlocation-based parameter member 430. These stored ambient sound profilesand processing parameters 425 may direct the system 140 to operate uponambient sound and/or secondary audio sources in a particular fashion.

The one or more sets of processing parameters 425 making up one or moreaudio sound profiles may have been stored in the audio parameter memory460 during prior exposure to ambient sound having particular profiles.The processing parameters 425 may direct the way in which ambient soundand secondary audio are treated by the time heuristic audio system 400.These settings may be across-the-board settings such as overall maximumor minimum volume or may be per-audio-source settings such that ambientaudio has reverb added, while secondary audio is clean. Similarly,ambient and/or secondary audio may be “spatialized” (made to sound asthough they are present at a particular location or distance from thehearer) based upon these processing parameters 425. More detail isprovided below.

For example, the user of the time heuristic audio system 400 maymanually set processing parameters 425 during a visit to a dance club.These processing parameters 425 may be saved in the audio parametermemory 460 in association with the profile of the ambient sound in thedance club. The processing parameters 425 may be saved in the audioparameter memory 430 in response to a user action, or may beautomatically “learned” by the active time heuristic audio system 400.Upon encountering similar ambient audio, the appropriate processingparameters 425 may be automatically retrieved from the audio parametermemory 460.

This heuristic learning process may take place based upon time-baseddata 455 received by a time-based heuristic learning andcharacterization function 458. The time-based data may be provided froma calendar, an email, or a text-based source available to a personalcomputing device 120, when compared with a clock, for example a clock ofthe personal computing device 120. The time-based data 455 may take theform of a calendar event indicating that a user is present at aparticular location, event, or premises. The time-based data 455 may beused by the time-based heuristic learning and characterization function458 in one of two ways.

First, the time-based heuristic learning and characterization function458 may make a determination whether the user is present at a particularlocation, event, or premises based upon the present time or availablesources of the user's current location, event, or premises. Thetime-based heuristic learning and characterization function 458 may, ifthe user has manually altered the processing parameters 425 of the audioprocessing function 410, take note of the current time, the associatedcurrent location, event, or premises in the audio parameter memory 460.In this way, if the user makes the same manual alteration to theprocessing parameters 425 more than a threshold number of times, theaudio parameter memory may be updated to reflect that those processingparameters 425 are to be used each time the time-based data 455indicates that the time has changed to the associated time or that theuser is present in the location, event, or premises.

Second, the time-based heuristic learning and characterization function458 may access the present time from, for example, a personal computingdevice 120 clock, or may access one or more data repositories for auser's present location, event attendance, or premises presenceperiodically or as a user changes locations. Based upon this time-baseddata 455, the audio processing function 410 may store instructions—userinput or learned heuristically—to use a particular set of processingparameters 425. In this way, the time-based heuristic learning andcharacterization function 458 may “learn” relevant times and placeswhich, based upon time-based data, may be used to automatically selectaudio processing parameters 425 for ambient sound and/or secondaryaudio.

Although location data is distinct from the time-based data 455, theheuristic learning and characterization function 458 may also “learn” orrely upon the geo-location function 440 or location data 435 to select aparticular set of processing parameters 425. Specifically, the function458 may rely upon all available data including both time-based data 455and location data 435 when making determinations of locations ofindividuals. Nonetheless, these two types of data are expressly distinctfrom one another as used herein.

While FIG. 4 depicts the audio parameter memory 460 and thelocation-based parameter memory 430 separately, these may be a commonmemory that associates each stored set of processing parameters 425 witha location, with an ambient sound profile, or both. Thus, one or both ofaudio parameters and location-based parameters may be taken into accountwhen selecting or suggesting processing parameters 425 for an timeheuristic audio system 400.

An adder 480 may add a secondary audio feed to the output from the audioprocessing function 410 to produce the digitized processed sound. Thesecondary audio feed may be received by the time heuristic audio system400 from an external source via a wireless communications link and thesecondary audio may be processed by the audio processing function 410before being added to the ambient sound. For example, a user at asporting event may receive a secondary audio feed of a sportscasterdescribing the event, which is then superimposed on the processedambient sound of the event itself. This superimposition of secondaryaudio may be, in part, controlled by time-based data 455 (e.g. ticketsto a sporting event stored in a user's email account) indicating thatthe user is present or plans to be present at the sporting event.

The depiction in FIG. 4 of the time heuristic audio system 400 as a setof functional blocks or elements does not imply any correspondingphysical separation or demarcation. All or portions of one or morefunctional elements may be located within a common circuit device ormodule. Any of the functional elements may be divided between two ormore circuit devices or modules. For example, all or portions of theaudio processing function 410, the analysis function 420, and the adder480 may be implemented within a time heuristic audio system packagedwithin an earbud or other housing configured to interface with a user'sear. The ambient sound characterization function 450, the audioparameter memory 460 and the location-based parameter memory 430 may bedistributed between time heuristic audio system and a personal computingdevice coupled to the time heuristic audio system by a wirelesscommunications link.

Next, FIG. 5 shows a functional block diagram of a time heuristic audiosystem 500. The system 500 is shown in functional blocks which may ormay not conform to individual elements of physical hardware. The system500 is made up of the audio processing system 510 and the personalcomputing device 520. While shown as distinct from one another,depending on the implementation, some or all aspects of the personalcomputing device 520 may be implemented within the audio processingsystem 510. Some or all of elements of the functional block diagram maybe encompassed within the audio processing system 110 or within thepersonal computing device 120. That is to say, the functions andprocessing described with reference to FIG. 5 may take place in whole orin part in one or both of these devices, with the final sound beingdelivered to one or more speakers within the audio processing system110.

The personal computing device 520, which may be personal computingdevice 120, includes time-based data sources 535, a clock 545, atime-based heuristic learning and characterization function 558, atime-based characterization memory 559, and a processing parametermemory 560. The audio processing system 510, which may be audioprocessing function 110, processes ambient and/or secondary audio asdirected by processing parameters used to guide that processing.

The user or external input 502 is manual or selected data identifying aparticular one or more processing parameters to be used by the audioprocessing system 510. The time-based heuristic learning andcharacterization function 558 is a function for both learning processingparameters associated with particular time-based data 555 and forinstructing the audio processing system 510 to use those learnedprocessing parameters when processing audio.

The time-based data sources 535 are sources from which time-based data555 is drawn. Examples of time-based data sources 535 include calendars,text messaging clients and email clients on the personal computingdevice 520. Other time-based data sources 535 may include cloud-basedsources such as email accounts with data stored on the web,web-accessible calendars, websites, and remotely-stored documents thatinclude time-based data 555.

Time-based data sources 535 include other sources of time-based data 555such as documents or hyperlinks included with emails, text messages, orinstant messages. The time-based data 555, such as a portable documentformat (PDF) ticket attached to an email or an embedded hyperlink in anemail, may indicate the time of an event so that the system may be awareof the user's presence at the event. Further, the PDF may also includeinformation pertaining to a particular artist or series of artistsappearing at, for example, a concert. Or, the PDF may includeinformation indicating the name of a stadium or venue where a sportingevent or performance is taking place. Still further alternatively, thePDF (or other time-based data source 535) may indicate that the user iswatching or planning to watch a particular movie (or a particulartheater for watching the movie) for which associated processingparameters 525 exist, and that may be loaded from processing parametermemory 560.

Time-based data sources 535 may include machine learning capabilitiessuch that less-specific cues may be required. For example, a text orinstant message on a particular date with the keyword “U2” identifying apopular Irish band of that name or, more specifically, using machinelanguage parsing techniques on a full phrase like “see you at U2tonight!” may be cross-referenced using Internet data to determine thatthere is a U2 concert later on the day of receipt of that text orinstant message. Thus, from this data, the system may extract time-baseddata 555 that indicates that a particular individual is likely going tobe present at that concert and may adjust audio processing parametersaccording to that time-based data 555 during the show.

The time-based data sources 535 may generate time-based data 555 that isused in conjunction with the ambient sound characterization function 450to select relevant processing parameters. For example, time-based data555 may be used to determine that the system is present at a particularconcert with a known start time. However, music may not actually beginexactly at the start time. So, the ambient sound characterizationfunction 450 may be use in conjunction with the time-based data 555 toselect processing parameters for the concert, but to awaitimplementation of the concert-based processing parameters until theambient sound characterization function 450 indicates that music hasactually begun. Until music has begun, the system may sit in a waitstate using default processing parameters awaiting the commencement ofmusic. This same type of wait state may be used in various types oftime-based data awaiting relevant ambient sound characterization by theambient sound characterization function 450.

Time-based data sources 535 may include other mobile applicationsoperating on the personal computing device 520 that can generate or haveaccess to time-based data 555. For example, a mobile application such asUber® by which a user requests a ride to a particular location mayprovide time-based data including a pick-up or drop-off location “pin”that identifies a place and an associated time. Time-based data 555 (andpotentially location data) may be drawn from the mobile application tobe used by the time-based heuristic learning and characterizationfunction 558 to select relevant processing parameters.

These types of information may be used, for example, by the time-basedcharacterization memory 559 to access parameters in the processingparameter memory 560 associated with a particular artists, event, orvenue. In this way, the time-based data 555 may be more than merely atime/location or a time/activity combination, but may further includeadditional data that is relevant to audio processing parameters.Processing parameters 525 stored in processing parameter memory 560 maybe user-set, set by an artist, set by a venue, set by an audiotechnician for a particular artist, venue, or event, or may becrowd-sourced such that if a sufficient number of users of the system ina location, at a venue, listening to an artist, or viewing a sportingevent select a particular set of processing parameters 525 (manually orautomatically) the same set of processing parameters 525 may beidentified by the time-based characterization memory 559 as associatedwith the time, event, artist, venue, or location.

The time-based data 555 may be or include the likelihood that a user ofthe system is sleeping or will be sleeping soon regardless of anyparticular location. Based upon prior user activity or likely userdesires, the system may automatically access processing parameters 525that lower the overall volume of ambient sound or otherwise cancelambient sound to aid a user in sleeping or to avoid unwanted externalaudio that may disturb sleeping patterns. In such a situation, the timebased data 555 may be or include a user's prior sleep patterns as inputby a user or as determined over time based upon settings of the systemor of personal computing devices in communication with the system. Forexample, a lack of interaction with the system or a personal computingdevice from certain hours may suggest sleep and, over time, be learnedby the system as associated with a typical sleep pattern for the user.Audio processing parameters 525 may be selected accordingly.

Time-based data 555 indicating that a particular artist (orevent-type—e.g. football, baseball, hockey, etc.) and at a particularvenue or location may indicate to the time-based characterization memory559 that a particular set of processing parameters 525 should beselected from the processing parameter memory 560. The processingparameter memory 560 may, in part, be or be formed by processingparameters 525 provided by third parties such as concert venues, eventmanagement companies, artists, sporting teams and similarly-situatedgroups for which specific processing parameters 525 may be relevant.

In some cases, these processing parameters 525 may identify one or moresecondary feeds such as a live feed from an audio technician's equipmentdirectly to a user's ears (rather than ambient audio for music), aparticular audio set for a movie or augmentation of ambient audio for amovie, a sportscaster's live broadcast super-imposed over the ambientsound of a stadium at a sporting event, and other, similar secondaryaudio sources.

The clock 545 provides the current time 548 on a periodic basis or uponrequest.

The time-based characterization memory 559 stores data pertaining touser or external input 502 that may be used to guide future automaticselection of processing parameters based upon time-based data 555. Thetime-based characterization memory 559 may also store the identity, aname for, a memory location of, or other data pertaining to oridentifying one or more processing parameters selected in relationshipto a particular set of time-based data 555. In this way, over time, thetime-based characterization memory 559 may come to store a number ofassociations between processing parameters and particular sets oftime-based data 555.

Further, if multiple processing parameters are identified as relevantbased upon a given set of time-based data 555, the system may enable aprocess of manual interaction with the personal computing device 120whereby a user can select one or more of those processing parameters forimplementation. This process may begin with an audio prompt to a user ofthe audio processing system 110 that indicates that interaction with thepersonal computing device 120 is required or, alternatively, may beginwith a visual cue on the personal computing device 120. Auditoryresponse or other non-visual responses, such as voice recognition inresponse to audio identification of associated processing parameters maybe available to a user. In this way, a user may hear, for example, thenames of processing parameter sets 1, 2, and 3, then speak audibly aselection of 1 and 3, whereby those two sets of processing parametersare selected. Exterior buttons, either on the personal computing device120 or the audio processing system 110 may be mapped to the selection ofparticular processing parameters identified audibly. Similarly, specificinteractions with exterior buttons, such as double-clicks or short, thenlong, clicks of a button, may indicate a particular response to theidentification of a processing parameter set.

As discussed above, the processing parameter memory stores processingparameters, like processing parameters 525, that instruct an audioprocessing system 510 in how to process a selected set of ambient and/orsecondary audio sources.

As discussed more fully below, the user or external input 502 may beprovided to the time-based heuristic learning and characterizationfunction 558 to, at least in part, inform the function 558 in whatprocessing parameters a user desires. This input may, for example, bethe manual selection of a pre-determined set of processing parameters,or may be the manual selection of a series of individual processingparameters to, thereby, manually create a set. This user or externalinput 502 may merely identify a set of processing parameters alreadystored in the processing parameter memory 560 or may include externalinput such as an identification of processing parameters provided by athird party as desirable.

This user or external input 502 may be stored by the function 558 in thetime-based characteristic memory 559, with the present time 548, asprovided by the clock 545, simultaneously noted and stored in the memory559. In addition, time-based data 555 provided from time-based datasources 535 may also be noted and stored in the memory 559. In this way,details regarding the selected processing parameters, the present time548, and the associated time-based data 555 (if any) may besimultaneously stored in memory 559 for later use in automaticallyselecting processing parameters.

The selected processing parameters 525 may be obtained from theprocessing parameter memory 560 and provided to the audio processingsystem 510 for operation upon any sources of sound in 505. Once actedupon by the audio processing system 510, using the processing parameters525, the processed sound out 515 is output by the audio processingsystem 510.

Once the time-based heuristic learning and characterization function 558has “learned” some time-based data 555 that is consistently used toselect one or more particular sets of processing parameters, user orexternal input 502 may no longer be required. Instead, time-based datasources 535 may be periodically consulted in conjunction with time 548from the clock 545 to determine that the time, date and/or day is thesame or that user is at the same event, premises, or location where theuser manually selected a set of processing parameters as describedabove. If the time-based data 555 and the time 548 correspond to theprior settings, the time-based heuristic learning and characterizationfunction 558 may refer to the time-based characterization memory 559 toobtain relevant processing parameters associated uniquely with thetime-based data 555 and the time 548 from the processing parametermemory 560. Thereafter, these processing parameters 525 may be providedto the audio processing system 510 without any user or external action.

Description of Processes

Referring now to FIG. 6, a flow chart of a method 600 for creating atime heuristic audio control is shown. The process begins at 605 andends at 695. The process 600 may occur many times as user input or otherexternal input altering processing parameters is received. The process600 results in the storage of the identity of time-based data andassociated processing parameters that may be linked so as to beautomatically selected at later times when the associated time-baseddata is received.

After the start 605, a determination is made whether a processingparameter has been selected at 615. If not (“no” at 615), then theprocess ends at 695.

If so (“yes” at 615), time-based data is accessed at 620. This may beaccessing a calendar, email, a short message service, external sourcesof time-based data such as web-based email, calendars, or similarsources. This may involve determining whether any of the time-basedsources indicates that a user of the system is presently at an event,location, or premises or, alternatively, may be a determination of thepresent time, day, and/or date so that it may, optionally, later beassociated with the processing parameter changes that have been maderepeatedly.

This time-based data is accessed so that the system may store the timebased data in conjunction with the selected processing parameters at630. This data may be stored, as described briefly above, in thetime-based heuristic memory 559 (FIG. 5).

Next, a determination is made, using data stored in the time-basedheuristic memory 559, whether there have been multiple selections of thesame (or essentially the same) processing parameters when relevanttime-based data is present at 635. This determination may merely becomparing a set of parameters to time-based data comprised of thepresent date/time (e.g. 10:00 am), to the present time and day (e.g.10:00 am on a Tuesday), to the present time and day and day of the month(e.g. 10:00 am on the second Tuesday of the month), to a specific timeand day (e.g. 10:00 am on a holiday morning), or to a specific event,premises, or location identified in time-based data from a time-baseddata source such as a calendar, email, a text message, or an instantmessage.

If there are multiple selections of the same processing parameters inconjunction with associated time-based data (e.g. always at 10:00 am onthe second Tuesday of the month or always when the user is present in a“meeting” with a certain person), then the processing parameters may bestored as automatic processing parameters to select at 640 when the sametime-based data is present in the future. For example, the processingparameters may be selected automatically every 10:00 am on the secondTuesday of the month or when the user is present in a “meeting” with acertain person.

The storage automatic selection of processing parameters at 640 may berelatively sophisticated in that it may store the processing parametersin conjunction with time-based data that is defined as one or moreif/then or case statements such that when each element is appropriatelymet, the automatic selection of processing parameters may take place.Over time, with further refinement, the automatic selection definitionmay be altered so as to conform to recent changes by a user.

Thereafter, the process ends at 695. Subsequent manual changes orexternal input of processing parameters may cause the process to beginagain as associated new time-based data may be introduced (e.g. anothermeeting, a different time, a different location) for which otherprocessing parameters may be desired by a user. In effect, this processmay occur continuously, with the system continuously monitoring for newtime-based data and associated processing parameters that may be“learned” heuristically and stored for later use.

Referring now to FIG. 7, a flow chart of a method for altering audioprocessing parameters in response to a time heuristic audio control isshown. The process 700 begins at 705 and ends at 795 once audioprocessing has begun using selected processing parameters. The process700 may be initiated periodically or based upon identification oftime-based data that may cause a change in the selection of processingparameters.

After the start 705, the time-based data is accessed at 710 by the timeheuristic learning and characterization function 558 so that thetime-based data may be used to determine if any changes to theprocessing parameters should be made. This may be accessing a time-baseddata source such as a calendar, email, or instant messaging service inorder to access or obtain time-based data.

Next, the current time is accessed at 720. This may include accessingthe current time, day, date, any holiday data or externally-availabledata that is not specific to an individual calendar, but is or may berelevant to the time heuristic learning and characterization function558 in determining whether new processing parameters should be used.

Next, the time heuristic learning and characterization function 558determines whether there are any current time-based processingparameters at 725. This process entails determining if any of thetime-based data accessed at 710 matches the current time accessed at 720such that new processing parameters should be used for processingambient and any secondary audio. If not (“no” at 725), then the processends at 795.

If so (“yes” at 725), then the processing parameters associated with thecurrent time-based data are identified at 730. This may involveaccessing the time-based characterization memory 559 to identify theprocessing parameters associated with the particular time-based data.Then, the processing parameters may be identified within the processingparameter memory 560 based upon the data in the time-basedcharacterization memory 559.

Once the processing parameters have been identified at 730, they aretransmitted to the audio processing system 510 at 740 so that audioprocessing may begin based upon those processing parameters identified.

The audio processing system 510 and/or the personal computing device 520may receive user interaction indicating that the user has elected tochange the automatically-selected processing parameters at 745. Thoughshown as immediately following transmission of the processingparameters, the audio processing system 510 may first begin performingaudio processing using the parameters and then accept changed processingparameters from a user. However, the option to alter those processingparameters exists from the moment they are automatically selected andtransmitted.

If a user makes a change to processing parameters (“yes” at 745), thenthe changes may be stored in the time-based characterization memory at750. These changes, particularly if they are made more than one time,may form the basis of updates to the processing parameters associatedwith a particular set of time-based data.

After any changes are stored at 750, or if no changes are detected (“no”at 745), the audio processing system 510 processes ambient and/orsecondary audio sources as directed by the processing parametersselected based upon the time-based data at 760. At any point, a user maymanually alter these processing parameters, but, these processingparameters may be automatically selected in response to relevanttime-based data as directed by the time heuristic control system.

The process then ends at 795, but may continue to be run periodically orin response to new time-based data indicating that processing parametersshould or may change.

Closing Comments

Throughout this description, the embodiments and examples shown shouldbe considered as exemplars, rather than limitations on the apparatus andprocedures disclosed or claimed. Although many of the examples presentedherein involve specific combinations of method acts or system elements,it should be understood that those acts and those elements may becombined in other ways to accomplish the same objectives. With regard toflowcharts, additional and fewer steps may be taken, and the steps asshown may be combined or further refined to achieve the methodsdescribed herein. Acts, elements and features discussed only inconnection with one embodiment are not intended to be excluded from asimilar role in other embodiments.

As used herein, “plurality” means two or more. As used herein, a “set”of items may include one or more of such items. As used herein, whetherin the written description or the claims, the terms “comprising”,“including”, “carrying”, “having”, “containing”, “involving”, and thelike are to be understood to be open-ended, i.e., to mean including butnot limited to. Only the transitional phrases “consisting of” and“consisting essentially of”, respectively, are closed or semi-closedtransitional phrases with respect to claims. Use of ordinal terms suchas “first”, “second”, “third”, etc., in the claims to modify a claimelement does not by itself connote any priority, precedence, or order ofone claim element over another or the temporal order in which acts of amethod are performed, but are used merely as labels to distinguish oneclaim element having a certain name from another element having a samename (but for use of the ordinal term) to distinguish the claimelements. As used herein, “and/or” means that the listed items arealternatives, but the alternatives also include any combination of thelisted items.

What is claimed is:
 1. A time heuristic audio system, comprising: ananalysis block that is configured to transform digitized ambient soundinto a frequency domain, and to determine a distribution of thedigitized ambient sound within an audible frequency spectrum; an ambientsound characterization block that is configured to develop an ambientsound profile based on the distribution of the digitized ambient soundwithin the audible frequency spectrum determined by the analysis block;a time-based heuristic learning and characterization block that isconfigured to access instructions related to a particular set ofprocessing parameters based on time-based data; an audio parametermemory that is configured to store sets of processing parameters, and isconfigured to retrieve one of the sets of processing parameters based onthe instructions related to the particular set of processing parametersaccessed by the time-based heuristic learning and characterization blockand on the ambient sound profile developed by the ambient soundcharacterization block; and an audio processing block that is configuredto generate processed ambient sound by processing the digitized ambientsound according to the one of the sets of processing parametersretrieved by the audio parameter memory.
 2. The time heuristic audiosystem of claim 1, further comprising: a user interface block that isconfigured to receive at least one manually entered processingparameter, wherein the audio processing block is further configured togenerate the processed ambient sound by processing the digitized ambientsound according to the at least one manually entered processingparameter.
 3. The time heuristic audio system of claim 1, furthercomprising: a geo-location block that is configured to provide locationdata; and a location-based parameter memory that is configured to storesets of location-based processing parameters, and is configured toretrieve one of the sets of location-based processing parameters basedon the location data, wherein the audio processing block is furtherconfigured to generate the processed ambient sound according to the oneof the sets of location-based processing parameters retrieved by thelocation-based parameter memory.
 4. The time heuristic audio system ofclaim 1, wherein the audio processing block is further configured togenerate a processed secondary audio feed by processing a secondaryaudio feed according to the one of the sets of processing parametersretrieved by the audio parameter memory, the time heuristic audio systemfurther comprising: an adder that adds the processed ambient sound andthe processed secondary audio feed to generate digitized processedsound, wherein the secondary audio feed corresponds to audio that is notaudible in a physical location where the time heuristic audio system ispresent.
 5. The time heuristic audio system of claim 4, wherein theaudio processing block is configured to process the digitized ambientsound according to a first set of settings, and to process the secondaryaudio feed according to a second set of settings.
 6. The time heuristicaudio system of claim 4, wherein the one of the sets of processingparameters define how the secondary audio feed is combined with thedigitized ambient sound.
 7. The time heuristic audio system of claim 1,wherein the audio processing block is further configured to receive asecond set of processing parameters from a cloud source, and to processthe digitized ambient sound according to the second set of processingparameters.
 8. The time heuristic audio system of claim 1, furthercomprising: a processor that is configured to implement the analysisblock, the ambient sound characterization block, the time-basedheuristic learning and characterization block, and the audio processingblock.
 9. The time heuristic audio system of claim 1, furthercomprising: a microphone that is configured to capture ambient sound;and an analog-to-digital converter that is configured to generate thedigitized ambient sound based on the ambient sound captured by themicrophone.
 10. The time heuristic audio system of claim 1, furthercomprising: a digital to analog converter that is configured to convertthe processed ambient sound into an analog signal; and a speaker that isconfigured to output sound corresponding to the analog signal convertedby the digital to analog converter.
 11. The time heuristic audio systemof claim 1, wherein the analysis block is further configured todetermine a presence of dominant sounds at particular frequencies, andwherein the ambient sound characterization block is further configuredto develop the ambient sound profile based on the presence of thedominant sounds at the particular frequencies determined by the analysisblock.
 12. The time heuristic audio system of claim 1, wherein thetime-based heuristic learning and characterization block is furtherconfigured to access the instructions related to the particular set ofprocessing parameters based on a determination that the time heuristicaudio system is present at one of a particular location, a particularevent, and a particular premises.
 13. The time heuristic audio system ofclaim 1, wherein the time-based heuristic learning and characterizationblock is further configured to access the instructions related to theparticular set of processing parameters based on a present time and apresent location of the time heuristic audio system stored in a datarepository.
 14. The time heuristic audio system of claim 1, wherein thetime-based heuristic learning and characterization block works inconjunction with the ambient sound characterization block to use a firstset of processing parameters prior to the ambient sound characterizationblock detecting music, and a second set of processing parameters afterthe ambient sound characterization block has detected the music.
 15. Amethod of controlling a time heuristic audio system, the methodcomprising: transforming, by an analysis block, digitized ambient soundinto a frequency domain; determining, by the analysis block, adistribution of the digitized ambient sound within an audible frequencyspectrum; developing, by an ambient sound characterization block, anambient sound profile based on the distribution of the digitized ambientsound within the audible frequency spectrum determined by the analysisblock; accessing, by a time-based heuristic learning andcharacterization block, instructions related to a particular set ofprocessing parameters based on time-based data; storing, by an audioparameter memory, sets of processing parameters; retrieving, by theaudio parameter memory, one of the sets of processing parameters basedon the instructions related to the particular set of processingparameters accessed by the time-based heuristic learning andcharacterization block and on the ambient sound profile developed by theambient sound characterization block; generating, by the audioprocessing block, processed ambient sound by processing the digitizedambient sound according to the one of the sets of processing parametersretrieved by the audio parameter memory.
 16. The method of claim 15,further comprising: receiving, by a user interface block, at least onemanually entered processing parameter; and generating, by the audioprocessing block, the processed ambient sound by processing thedigitized ambient sound according to the at least one manually enteredprocessing parameter.
 17. The method of claim 15, further comprising:providing, by a geo-location block, location data; storing, by alocation-based parameter memory, sets of location-based processingparameters; retrieving, by the location-based parameter memory one ofthe sets of location-based processing parameters based on the locationdata; and generating, by the audio processing block, the processedambient sound according to the one of the sets of location-basedprocessing parameters retrieved by the location-based parameter memory.18. The method of claim 15, further comprising: generating, by the audioprocessing block, a processed secondary audio feed by processing asecondary audio feed according to the one of the sets of processingparameters retrieved by the audio parameter memory; and adding, by anadder, the processed ambient sound and the processed secondary audiofeed to generate digitized processed sound, wherein the secondary audiofeed corresponds to audio that is not audible in a physical locationwhere the time heuristic audio system is present.
 19. The method ofclaim 15, further comprising: capturing, by a microphone, ambient sound;and generating, by an analog-to-digital converter, the digitized ambientsound based on the ambient sound captured by the microphone.
 20. Themethod of claim 15, further comprising: converting, by a digital toanalog converter, the processed ambient sound into an analog signal; andoutputting, by a speaker, sound corresponding to the analog signalconverted by the digital to analog converter.